The work [1]} proposed a two-stage framework for joint spectral-spatial HSIC which can directly extract both spectral and spatial features instead of independently concatenating them. The first stage of the proposed network is comprised of a CNN and softmax normalization that adaptively learns the weights for input patches and extracts joint shallow features. These shallow features are then fed to a network of Stacked Autoencoder (SAE) to obtain deep hierarchical features and final classification is performed with a Multinomial Logistic Regression (MLR) layer. A \(3D\) -CNN model was introduced in [2]} to jointly exploit spectral-spatial features from HSI and to validate its performance comparison is performed with spectral-based DBN, SAE, and \(2D\) -spatial CNN for HSIC. The work [3]} introduced a bilinear fusion mechanism over the two branches of squeeze operation based on the global and max-pooling whereas the excitation operation is performed with the fused output of squeeze operation.
